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聚类模型在客户关系管理中的应用以及对特征提取的探讨
引用本文:谭元戎,孙剑平.聚类模型在客户关系管理中的应用以及对特征提取的探讨[J].技术经济,2007,26(5):51-56,83.
作者姓名:谭元戎  孙剑平
作者单位:南京理工大学,经济管理学院,南京,210094
摘    要:随着数据挖掘技术的发展和信息的增长,企业和公司开始运用数据挖掘技术来分析和处理他们在商业活动中积累的关于客户的大量数据,以从中发现重要的规律,来指导公司的营销策略。客户聚类就是一个重要的问题。它根据客户的个人属性和行为属性,把相似的客户群聚集起来。公司可以根据不同类型的客户作出不同的营销策略。本文讨论了有关聚类模型的两个问题。第一,介绍了两种经典的聚类算法,以及他们的复杂度。并讨论它们在客户关系管理中的应用和有效性;第二,讨论了特征提取在聚类过程中的必要性,并给出了如何在聚类模型中进行特征提取的有效算法。

关 键 词:聚类  客户关系管理  数据挖掘  特征提取  非监督学习
文章编号:1002-980X(2007)05-0051-06
修稿时间:2006-01-05

The Practice of Clustering in CRM and Discussion on Characteristic Distill
TAN Yuan-rong,SUN Jian-ping.The Practice of Clustering in CRM and Discussion on Characteristic Distill[J].Technology Economics,2007,26(5):51-56,83.
Authors:TAN Yuan-rong  SUN Jian-ping
Abstract:With the development of Data Mining and the growth of information, many enterprises and cooperates begin to use the Data Mining technique to analyze and process the large amount of data which is accumulated in the business activities in order to find out important rules for marketing strategy. Customer clustering is a crucial issue. According to customer clustering, similar customer groups cluster on the basis of their personal and behavioral attributes. Then, the enterprises can work out different marketing strategies and apply them to each customer group respectively. The thesis focuses on two questions about clustering formation. First, several typical clustering algorithms and their complexities are introduced. Meanwhile, the application and efficiency of the clustering algorithm are discussed in it, and then the effectual arithmetic on how to carry through characteristic distill in the clustering formation is given out.
Keywords:clustering  customer relationship management(CRM)  data mining(DM)  characteristic distill  unsupervised learning(UL)
本文献已被 CNKI 维普 万方数据 等数据库收录!
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